The most important feature of our models is the ability to incorporate the dependence on cosmological parameters. The late-time structure evolution is sensitive to the expansion history of the Universe, i.e., to the expansion rate \(H_0 = 100h \, \mathrm {km}/\mathrm {s}/\mathrm {Mpc}\)  and the matter density parameter \(\Omega _\mathrm {m}\) . The former has been accounted for by using proper units that include \(h\) , thus we only need to include \(\Omega _\mathrm {m}\)  as an input to our networks. We achieve this following StyleGAN2 [1]}, in which style parameters are incorporated by weight modulations and demodulations on the convolution kernels. In the modulation phase, the style parameters (here \(\Omega _\mathrm {m}\) ) are first affine transformed to the same dimension as the number of input convolution channels, and are then multiplied to the kernel of each channel, respectively. In the demodulation phase, the convolution kernels are renormalized by their fan-ins, so that propagating data do not explode or vanish. We refer readers to Eq. (1–3) in Ref. [1]} for more details.
